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Boosting Zero-shot Classification with Synthetic Data Diversity via Stable Diffusion

J. Shipard; A. Wiliem; K. Nguyen; W. Xiang; C. Fookes


Recent research has shown it is possible to perform zero-shot classification tasks by training a classifier with synthetic data generated by a diffusion model. However, the performance of this approach is still inferior to that of recent vision-language models. It has been suggested that the reason for this is a domain gap between the synthetic and real data. In our work, we show that this domain gap is not the main issue, and that diversity in the synthetic dataset is more important. We propose a \textit{bag of tricks} to improve diversity and are able to achieve performance on par with one of the vision-language models, CLIP. More importantly, this insight allows us to endow zero-shot classification capabilities on any classification model.

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